from classify_chat_helper import call_classification_llm
from classify_parameters import TRACKED_FIELDS, ALLOWED_STORY_TOPICS, TOPIC_DESCRIPTIONS
from db_user import load_user_info, save_user_info, update_country
from db_5_process_session import _load_history

_VALID_TOPICS = set(TOPIC_DESCRIPTIONS.keys())


RECENT_HISTORY_LIMIT = 5

def analyze_message(user_id: str, query: str, character_id: str = "socrates"):
    # --- Load current user_info for this user ---
    user_info = load_user_info(user_id)

    profile = user_info.get("user_profile", {})
    missing_fields = [f for f in ["name", "living_country", "origin_country"] if not profile.get(f)]

    # --- Trim history to the most recent turns ---
    history = _load_history("chat_history_short", user_id)
    all_messages = []
    for session in history.get("sessions", []):
        all_messages.extend(session.get("messages", []))

    recent_history = all_messages[-RECENT_HISTORY_LIMIT:] if all_messages else []
    
    # --- Call LLM for classification + extraction ---
    analysis = call_classification_llm(query, recent_history, user_info, missing_fields, character_id=character_id)

    # Strip empty/blank values — classifier returns all fields, most empty
    raw_extracted = analysis.get("extracted_user_info", {})
    extracted_info = {
        k: v for k, v in raw_extracted.items()
        if v not in (None, "", [], {})
    }
    relevant_missing = analysis.get("relevant_missing_fields", [])
    topic = analysis.get("topic", "personal")
    # Guard: LLM sometimes bleeds response_mode values (e.g. "dialogic") into topic.
    if topic not in _VALID_TOPICS:
        topic = "personal"
    response_mode = analysis.get("response_mode", "dialogic")
    topic_for_tale = analysis.get("topic_for_tale", "none")
    if topic_for_tale not in ALLOWED_STORY_TOPICS:
        topic_for_tale = "none"

    # --- Update user_info in Supabase only when genuinely new info was extracted ---
    if extracted_info:
        _prev_living  = profile.get("living_country", "")
        _prev_origin  = profile.get("origin_country", "")
        profile.update(extracted_info)
        user_info["user_profile"] = profile
        save_user_info(user_info, user_id)

        # Only update countries when the LLM extracted a value that differs from what was stored
        if "living_country" in extracted_info and extracted_info["living_country"] != _prev_living:
            update_country("living", extracted_info["living_country"], user_id)
        if "origin_country" in extracted_info and extracted_info["origin_country"] != _prev_origin:
            update_country("origin", extracted_info["origin_country"], user_id)

    return {
        "topic": topic,
        "response_mode": response_mode,
        "user_info": user_info,
        "relevant_missing": relevant_missing,
        "topic_for_tale": topic_for_tale,
        "chunks": [],
        "needs_news_fetch": analysis.get("needs_news_fetch", False),
        "news_topic": analysis.get("news_topic", ""),
        "news_question": analysis.get("news_question", ""),
        "news_temporal_context": analysis.get("news_temporal_context", ""),
        "socratic_trigger": analysis.get("socratic_trigger", "none") or "none",
        "trigger_subtype": analysis.get("trigger_subtype", "") or "",
        "socratic_alignment": analysis.get("socratic_alignment", "none") or "none",
    }