model_utils.py

# model_utils.py
from typing import List, Optional
import re

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

import qa_store
from loader import load_curriculum, load_manual_qa, rebuild_combined_qa

# -----------------------------
# Model
# -----------------------------
MODEL_NAME = "SeaLLMs/SeaLLMs-v3-1.5B-Chat"

tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)

device = "cuda" if torch.cuda.is_available() else "cpu"

model = AutoModelForCausalLM.from_pretrained(
    MODEL_NAME,
    torch_dtype=torch.float32,
).to(device)

model.eval()


# Load data once at import time
load_curriculum()
load_manual_qa()
rebuild_combined_qa()

SYSTEM_PROMPT = (
    "ທ່ານແມ່ນຜູ້ຊ່ວຍເຫຼືອດ້ານປະຫວັດສາດຂອງປະເທດລາວ "
    "ສໍາລັບນັກຮຽນຊັ້ນ ມ.1. "
    "ຕອບແຕ່ພາສາລາວ ໃຫ້ຕອບສັ້ນໆ 2–3 ປະໂຫຍກ ແລະເຂົ້າໃຈງ່າຍ. "
    "ໃຫ້ອີງຈາກຂໍ້ມູນຂ້າງລຸ່ມນີ້ເທົ່ານັ້ນ. "
    "ຖ້າຂໍ້ມູນບໍ່ພຽງພໍ ຫຼືບໍ່ຊັດເຈນ ໃຫ້ບອກວ່າບໍ່ແນ່ໃຈ."
)


def retrieve_context(question: str, max_entries: int = 2) -> str:
    """
    Simple keyword retrieval over textbook entries.
    """
    if not qa_store.ENTRIES:
        return qa_store.RAW_KNOWLEDGE

    q = question.lower().strip()
    terms = [t for t in re.split(r"\s+", q) if len(t) > 1]

    if not terms:
        chosen = qa_store.ENTRIES[:max_entries]
        return "\n\n".join(
            f"[ຊັ້ນ {e.get('grade','')}, ບົດ {e.get('chapter','')}, "
            f"ຫົວຂໍ້ {e.get('section','')} – {e.get('title','')}]\n{e['text']}"
            for e in chosen
        )

    scored = []

    for e in qa_store.ENTRIES:
        text = e.get("text", "")
        title = e.get("title", "")
        kws = e.get("keywords", [])
        topic = e.get("topic", "")

        base = (text + " " + title).lower()
        score = 0

        for t in terms:
            score += base.count(t)

        for kw in kws:
            kw_lower = kw.lower()
            for t in terms:
                if t in kw_lower:
                    score += 2

        if topic and any(t in topic for t in terms):
            score += 1

        if score > 0:
            scored.append((score, e))

    scored.sort(key=lambda x: x[0], reverse=True)
    top_entries = [e for _, e in scored[:max_entries]]

    if not top_entries:
        top_entries = qa_store.ENTRIES[:max_entries]

    context_blocks = []
    for e in top_entries:
        header = (
            f"[ຊັ້ນ {e.get('grade','')}, "
            f"ບົດ {e.get('chapter','')}, "
            f"ຫົວຂໍ້ {e.get('section','')} – {e.get('title','')}]"
        )
        context_blocks.append(f"{header}\n{e.get('text','')}")

    return "\n\n".join(context_blocks)


def build_prompt(question: str) -> str:
    context = retrieve_context(question)
    return f"""{SYSTEM_PROMPT}

ຂໍ້ມູນອ້າງອີງ:
{context}

ຄຳຖາມ: {question}

ຄຳຕອບດ້ວຍພາສາລາວ:"""


def generate_answer(question: str) -> str:
    prompt = build_prompt(question)
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    with torch.no_grad():
        outputs = model.generate(
            **inputs,
            max_new_tokens=160,
            do_sample=False,
        )

    generated_ids = outputs[0][inputs["input_ids"].shape[1]:]
    answer = tokenizer.decode(generated_ids, skip_special_tokens=True).strip()

    # ✅ Enforce 2–3 short sentences
    # `re` is already imported at the top of this file
    sentences = re.split(r"(?<=[\.?!…])\s+", answer)
    short_answer = " ".join(sentences[:3]).strip()

    return short_answer if short_answer else answer



def answer_from_qa(question: str) -> Optional[str]:
    """
    1) exact match in QA_INDEX
    2) fuzzy match via word overlap with ALL_QA_KNOWLEDGE
    """
    norm_q = qa_store.normalize_question(question)
    if not norm_q:
        return None

    if norm_q in qa_store.QA_INDEX:
        return qa_store.QA_INDEX[norm_q]

    q_terms = [t for t in norm_q.split(" ") if len(t) > 1]
    if not q_terms:
        return None

    best_score = 0
    best_answer: Optional[str] = None

    for item in qa_store.ALL_QA_KNOWLEDGE:
        stored_terms = [t for t in item["norm_q"].split(" ") if len(t) > 1]
        overlap = sum(1 for t in q_terms if t in stored_terms)
        if overlap > best_score:
            best_score = overlap
            best_answer = item["a"]

    # require at least 2 overlapping words to accept fuzzy match
    if best_score >= 2:
        # optional: log when fuzzy match is used
        print(f"[FUZZY MATCH] score={best_score} -> {best_answer[:50]!r}")
        return best_answer

    return None


def laos_history_bot(message: str, history: List) -> str:
    """
    Main chatbot function for Student tab.
    """
    if not message.strip():
        return "ກະລຸນາພິມຄໍາຖາມກ່ອນ."

    direct = answer_from_qa(message)
    if direct:
        # later you can make this dynamic from the dataset
        meta = "[ຊັ້ນ M1, ບົດ 1]"  
        return f"{meta} {direct}"


    try:
        answer = generate_answer(message)
    except Exception as e:  # noqa: BLE001
        return f"ລະບົບມີບັນຫາ: {e}"

    meta = "[ຊັ້ນ M1, ບົດ 1]"  # placeholder, later make dynamic
    return f"{meta} {answer}"