# app.py
# 法遵自評覆核（Hugging Face Spaces 版）
# - PDF 讀取：先用 PyMuPDF 擷取文字；若該頁無文字則以 PaddleOCR（lang="ch"）進行 OCR（含繁體）
# - LLM：本地 Hugging Face Transformers（預設 meta-llama/Meta-Llama-3.1-8B-Instruct），首次自動下載到 /data/hf
# - 自評檢核規則：預設從 config/config.json 的 DEFAULT_RULES 載入（可在 UI 中編輯）
# - 每個函式皆加上 try/except 與註解；重要步驟 print log 便於除錯
# - UI：內規多檔上傳（可預覽）、自評規則（必填）、自評文字或自評附檔（二擇一）、Debug Log 顯示切換

import os
import io
import re
import json
import time
from typing import List, Dict, Tuple, Optional

import gradio as gr
import fitz  # PyMuPDF
import numpy as np
from PIL import Image

# HF 變數與快取位置（首次下載後可持久化）
os.environ.setdefault("TRANSFORMERS_CACHE", "/data/hf")
os.environ.setdefault("HF_HOME", "/data/hf")

# -----------------------------
# 設定檔載入（含預設值與錯誤處理）
# -----------------------------
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
CONFIG_PATH = os.path.join(BASE_DIR, "Config", "config.json")

# 預設 fallback（若找不到 config.json）
_default_cfg = {
    "DEFAULT_RULES": "【請在此貼上你的檢核規則】",
    "DEMO_PROMPT_INSTRUCTIONS": (
        "你是一位法遵／合規覆核專家。請依『檢核規範』逐點比對『文件全文』，並嚴格以 JSON 回覆："
        '{"符合情況":"符合|不符合|部分符合","原因":[],"改進建議":[],"規則逐點檢核":[{"規則編號":"","規則內容":"",'
        '"判斷":"符合|不符合|部分符合","理由":"","建議":""}]}'
    ),
    "SYSTEM_MESSAGE": "你是嚴謹的法遵覆核專家，請以審計可追溯為原則回覆並僅輸出 JSON。",
    "temperature": 0.2,
    "MAX_TOKENS_INPUT": 100000
}

try:
    with open(CONFIG_PATH, "r", encoding="utf-8") as f:
        cfg = json.load(f) or {}
    print(f"[CONFIG] Loaded: {CONFIG_PATH}")
except FileNotFoundError:
    print(f"[CONFIG][WARN] {CONFIG_PATH} not found, using defaults.")
    cfg = dict(_default_cfg)
except Exception as e:
    print(f"[CONFIG][ERROR] Failed to load config.json: {e}. Using defaults.")
    cfg = dict(_default_cfg)

DEFAULT_RULES = (cfg.get("DEFAULT_RULES") or _default_cfg["DEFAULT_RULES"]).strip()
DEMO_PROMPT_INSTRUCTIONS = (cfg.get("DEMO_PROMPT_INSTRUCTIONS") or _default_cfg["DEMO_PROMPT_INSTRUCTIONS"]).strip()
SYSTEM_MESSAGE = (cfg.get("SYSTEM_MESSAGE") or _default_cfg["SYSTEM_MESSAGE"]).strip()
TEMPERATURE = float(cfg.get("temperature", _default_cfg["temperature"]))
MAX_TOKENS_INPUT = int(cfg.get("MAX_TOKENS_INPUT", _default_cfg["MAX_TOKENS_INPUT"]))

# -----------------------------
# PaddleOCR 初始化（lang='ch' 同時支援簡/繁）
# -----------------------------
import torch
from paddleocr import PaddleOCR

OCR = None
def _init_ocr() -> Optional[PaddleOCR]:
    """初始化 PaddleOCR（採用 lang='ch'，支援簡/繁；自動偵測 GPU/CPU）。"""
    try:
        print("[OCR] Initializing PaddleOCR (lang='ch', PP-OCRv4)")
        ocr = PaddleOCR(
            lang="ch",
            use_angle_cls=True,
            use_gpu=torch.cuda.is_available(),
            ocr_version="PP-OCRv4",
            show_log=False
        )
        print("[OCR] Ready.")
        return ocr
    except Exception as e:
        print(f"[OCR][ERROR] init failed: {e}")
        return None

# -----------------------------
# 本地 LLaMA（Transformers）
# -----------------------------
from transformers import AutoModelForCausalLM, AutoTokenizer

LOCAL_MODEL_ID = os.getenv("LOCAL_MODEL_ID", "meta-llama/Meta-Llama-3.1-8B-Instruct")
HF_TOKEN = os.getenv("HF_TOKEN", None)

_hf_tok = None
_hf_model = None

def _ensure_local_model(logs: Optional[List[str]] = None) -> None:
    """確保本地模型已載入（首次呼叫時自動下載/載入到 /data/hf）。"""
    global _hf_tok, _hf_model
    try:
        if _hf_tok is not None and _hf_model is not None:
            return
        if logs is not None:
            logs.append(f"[LOCAL LLM] Loading model: {LOCAL_MODEL_ID}")
        print(f"[LLM] Loading {LOCAL_MODEL_ID} (cache={os.environ.get('TRANSFORMERS_CACHE')})")
        _hf_tok = AutoTokenizer.from_pretrained(
            LOCAL_MODEL_ID, use_fast=True, cache_dir=os.environ["TRANSFORMERS_CACHE"], token=HF_TOKEN
        )
        _hf_model = AutoModelForCausalLM.from_pretrained(
            LOCAL_MODEL_ID,
            torch_dtype=torch.bfloat16 if torch.cuda.is_available() else torch.float32,
            device_map="auto",
            cache_dir=os.environ["TRANSFORMERS_CACHE"],
            token=HF_TOKEN
        )
        if logs is not None:
            logs.append("[LOCAL LLM] Model ready.")
        print("[LLM] Ready.")
    except Exception as e:
        if logs is not None:
            logs.append(f"[LOCAL LLM][ERROR] load failed: {e}")
        print(f"[LLM][ERROR] load failed: {e}")
        raise
# ---------- Robust JSON parsing helpers ----------
def _strip_code_fences(s: str) -> str:
    s = s.strip()
    if s.startswith("```"):
        s = s[3:]
        if "```" in s:
            s = s.split("```", 1)[0]
    s = s.replace("```json", "").replace("```JSON", "").strip("` \n\r\t")
    return s

def _extract_first_brace_block(s: str) -> str:
    start = s.find("{")
    if start == -1:
        return s
    depth = 0
    for i in range(start, len(s)):
        if s[i] == "{":
            depth += 1
        elif s[i] == "}":
            depth -= 1
            if depth == 0:
                return s[start:i+1]
    return s

def safe_parse_json(text: str) -> dict:
    """
    先嚴格 json.loads；失敗則：
    1) 去掉 code fences/markdown
    2) 擷取第一個平衡的 {...}
    3) 嘗試 json5（允許單引號、尾逗號）
    4) 修補全形/花式引號與 BOM；必要時把整體單引號轉雙引號
    """
    import json as _json

    # 直接試一次
    try:
        return _json.loads(text)
    except Exception:
        pass

    s = _strip_code_fences(text)
    s = _extract_first_brace_block(s)

    try:
        return _json.loads(s)
    except Exception:
        pass

    # 可選：json5（若未安裝會直接跳過）
    try:
        import json5  # type: ignore
        return json5.loads(s)
    except Exception:
        pass

    # 修補引號與 BOM
    repaired = (
        s.replace("\u201c", '"').replace("\u201d", '"')
         .replace("\u2018", "'").replace("\u2019", "'")
         .replace("\ufeff", "").strip()
    )
    if "'" in repaired and '"' not in repaired:
        repaired = repaired.replace("'", '"')
    return _json.loads(repaired)
def extract_model_reply(full_text, prompt):
    """
    從模型完整輸出中，移除 prompt 和任何 system、assistant 等前置內容
    """
    try:
        # 如果模型有把 prompt 或 system 一起回顯，先找最後一次 user 提問位置
        markers = ["user", "User", "使用者", prompt.strip()]
        last_pos = -1
        for m in markers:
            pos = full_text.rfind(m)
            if pos > last_pos:
                last_pos = pos

        # 從最後 marker 後面開始取內容
        if last_pos != -1:
            reply = full_text[last_pos + len(markers[-1]):]
        else:
            reply = full_text

        # 移除多餘空白與換行
        return reply.strip()
    except Exception as e:
        print(f"[extract_model_reply 錯誤] {e}")
        return full_text.strip()

# === 放在 safe_parse_json 之後：用「正則」擷取 full_text 中最後一個完整 JSON 物件 ===
try:
    import regex as re2  # 第三方 regex，支援遞迴 (?R)
except Exception:
    re2 = None

def extract_last_json_block(text: str) -> Optional[str]:
    """
    以 regex 擷取最後一個平衡的大括號 JSON 物件：
    - 優先使用第三方 `regex` 的遞迴 (?R) 來比對平衡大括號
    - 若無法使用 `regex`，改用手動堆疊法做 fallback
    回傳：最後一個 JSON 物件字串；若找不到回傳 None
    """
    try:
        s = _strip_code_fences(text)

        # 1) 使用 regex (?R) 遞迴：{\n  ... { ... } ... \n}
        if re2 is not None:
            pattern = re2.compile(r"\{(?:[^{}]|(?R))*\}", flags=re2.DOTALL)
            matches = [m.group(0) for m in pattern.finditer(s)]
            return matches[-1] if matches else None

        # 2) 無 regex 模組 → 手動掃描平衡大括號
        blocks = []
        depth = 0
        start = None
        for i, ch in enumerate(s):
            if ch == "{":
                if depth == 0:
                    start = i
                depth += 1
            elif ch == "}":
                if depth > 0:
                    depth -= 1
                    if depth == 0 and start is not None:
                        blocks.append(s[start:i+1])
                        start = None
        return blocks[-1] if blocks else None
    except Exception as e:
        print(f"[JSON-EXTRACT][ERROR] {e}")
        return None

        
def call_llm(messages: List[dict], model: str, logs: List[str]) -> dict:
    """
    保留原名稱 call_llm，但改為本地 LLaMA。
    嚴格要求僅輸出 JSON；若混入其他文本，會正則擷取第一個 {...}。
    """
    start_time = time.time()  # 計時開始
    try:
        _ensure_local_model(logs)
        # 準備 chat prompt（加上 JSON 輸出約束）
        sys_txt = messages[0].get("content", "") if messages else ""
        usr_txt = messages[1].get("content", "") if len(messages) > 1 else ""
        extra_rules = "\n\n請務必只輸出單一 JSON 物件，不得包含任何 JSON 之外的文字或符號。"
        print('準備 chat prompt（加上 JSON 輸出約束）')
        chat = [
            {"role": "system", "content": sys_txt},
            {"role": "user", "content": usr_txt + extra_rules}
        ]
        print(f"user content:{usr_txt + extra_rules}")
        
        prompt = _hf_tok.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)

        inputs = _hf_tok(prompt, return_tensors="pt").to(_hf_model.device)
        print("inputs")
        with torch.no_grad():
            out_ids = _hf_model.generate(
                **inputs,
                max_new_tokens=1024,
                temperature=float(TEMPERATURE),
                do_sample=(float(TEMPERATURE) > 0),
                eos_token_id=_hf_tok.eos_token_id,
                pad_token_id=_hf_tok.eos_token_id
            )
        print("torch.no_grad")
        # 解碼生成內容後
        full_text = _hf_tok.decode(out_ids[0], skip_special_tokens=True)
        
        

         # ★ 使用 regex/堆疊法：從 full_text 擷取「最後一個」完整 JSON 物件
        candidate = extract_last_json_block(full_text)
        gen_text = candidate if candidate is not None else full_text  # 若找不到就用原文（後續 safe_parse_json 仍會嘗試）
        logs.append(f"[LOCAL LLM] raw_len={len(gen_text)}")
        logs.append(f"[LOCAL LLM] gen_text={gen_text}")
        logs.append(f"[LOCAL LLM] prompt={prompt}")
        logs.append(f"[LOCAL LLM] full_text={full_text}")
          # 強韌解析
        try:
            data = safe_parse_json(gen_text)
            logs.append("[LOCAL LLM] JSON 解析成功")
            return data
        except Exception as jerr:
            logs.append(f"[LOCAL LLM] JSON 解析失敗：{jerr}")
            return {
                "符合情況": "部分符合",
                "原因": [f"模型輸出非合法 JSON：{str(jerr)}"],
                "改進建議": ["請調整提示詞，要求嚴格輸出 JSON（雙引號、無註解、無多餘文字）。"],
                "規則逐點檢核": []
            }
        return data
    except Exception as e:
        logs.append(f"[LOCAL LLM] JSON 解析失敗：{e}")
        return {
            "符合情況": "部分符合",
            "原因": [f"本地模型錯誤：{e}"],
            "改進建議": ["請檢查 Hugging Face 權杖與模型權限、或改用較小模型。"],
            "規則逐點檢核": []
        }
    finally:
        elapsed = time.time() - start_time
        logs.append(f"[LOCAL LLM] 本次執行耗時：{elapsed:.2f} 秒")
# -----------------------------
# 檔案讀取：TXT / PDF（含 OCR fallback）
# -----------------------------
def _read_text_file(path: str) -> str:
    """讀取 TXT：嘗試多種常見編碼（台灣環境可能遇到 cp950/big5）。"""
    try:
        print(f"[READ] TXT: {os.path.basename(path)}")
        encodings = ("utf-8", "utf-8-sig", "cp950", "big5", "latin1")
        for enc in encodings:
            try:
                with open(path, "r", encoding=enc, errors="ignore") as f:
                    return f.read()
            except Exception:
                continue
        with open(path, "rb") as f:
            return f.read().decode("utf-8", errors="ignore")
    except Exception as e:
        print(f"[READ][ERROR] TXT {path}: {e}")
        return ""

def _ensure_ocr_ready():
    """Lazy 初始化 OCR（避免在未用到時佔資源）。"""
    global OCR
    if OCR is None:
        OCR = _init_ocr()

def _ocr_page_text(page) -> str:
    """將 PDF 頁面轉圖後以 OCR 讀取文字。"""
    try:
        _ensure_ocr_ready()
        if OCR is None:
            return ""
        pix = page.get_pixmap(dpi=240)
        img = Image.open(io.BytesIO(pix.tobytes("png"))).convert("RGB")
        res = OCR.ocr(np.array(img), cls=True)
        if not res or not res[0]:
            return ""
        return "\n".join([line[1][0] for line in res[0]]).strip()
    except Exception as e:
        print(f"[OCR][ERROR] {e}")
        return ""

def _read_pdf_text(path: str) -> Tuple[str, int]:
    """
    讀取 PDF：若該頁抽不到文字（可能是掃描影像），則以 OCR 進行辨識。
    回傳：(全文, 頁數)
    """
    try:
        print(f"[READ] PDF: {os.path.basename(path)}")
        parts: List[str] = []
        with fitz.open(path) as doc:
            for page in doc:
                txt = (page.get_text("text") or "").strip()
                if len(txt) < 20:  # 低於門檻判定影像頁
                    ocr_txt = _ocr_page_text(page)
                    parts.append(ocr_txt)
                else:
                    parts.append(txt)
            return "\n".join(parts).strip(), len(doc)
    except Exception as e:
        print(f"[READ][ERROR] PDF {path}: {e}")
        return "", 0

def _read_file_to_text(file_path: Optional[str]) -> Tuple[str, str]:
    """統一入口：根據副檔名分派至 TXT/PDF 讀取函式。"""
    try:
        if not file_path:
            raise ValueError("未收到檔案路徑")
        file_path = os.fspath(file_path)
        if not os.path.exists(file_path):
            raise FileNotFoundError(f"檔案不存在：{file_path}")
        fname = os.path.basename(file_path)
        ext = os.path.splitext(fname)[1].lower()
        if ext == ".txt":
            content = _read_text_file(file_path)
            return content, f"讀取 TXT：{fname}（長度：{len(content)}）"
        elif ext == ".pdf":
            content, pages = _read_pdf_text(file_path)
            return content, f"讀取 PDF：{fname}（頁數：{pages}；長度：{len(content)}）"
        else:
            raise ValueError("僅支援 .txt 或 .pdf")
    except Exception as e:
        print(f"[READ][ERROR] {e}")
        return "", f"[ERROR] {e}"

# -----------------------------
# 多檔輔助：清單摘要 / 快取與預覽
# -----------------------------
def _read_multi(paths: Optional[List[str]]) -> Dict[str, str]:
    """批次讀取多檔，回傳 {檔名: 文字內容}，忽略讀取錯誤。"""
    cache: Dict[str, str] = {}
    try:
        if not paths:
            return cache
        for p in paths:
            if not p:
                continue
            try:
                name = os.path.basename(os.fspath(p))
                text, _ = _read_file_to_text(p)
                cache[name] = text
            except Exception as e:
                print(f"[READ][WARN] skip {p}: {e}")
                continue
        return cache
    except Exception as e:
        print(f"[READ_MULTI][ERROR] {e}")
        return cache

def _summarize_paths(paths: Optional[List[str]]) -> str:
    """列出上傳檔案清單（含大小），供 UI 顯示。"""
    try:
        if not paths:
            return "（尚未上傳任何檔案）"
        lines = []
        for p in paths:
            try:
                name = os.path.basename(os.fspath(p))
                size = os.path.getsize(os.fspath(p))
                lines.append(f"- {name}（{size} bytes）")
            except Exception:
                lines.append(f"- {os.path.basename(str(p))}")
        return "\n".join(lines)
    except Exception as e:
        print(f"[SUMMARY][ERROR] {e}")
        return "（清單生成失敗）"

def on_files_change(paths: Optional[List[str]]):
    """Gradio callback：檔案更新 → 更新清單 / 勾選選項 / 預覽區 / 快取。"""
    try:
        cache = _read_multi(paths)
        choices = list(cache.keys())
        return (
            _summarize_paths(paths),
            gr.update(choices=choices, value=[]),
            gr.update(value="", visible=False),
            cache,
        )
    except Exception as e:
        print(f"[FILES_CHANGE][ERROR] {e}")
        return "（更新失敗）", gr.update(), gr.update(), {}

def on_show_toggle(selected_names: List[str], cache: Dict[str, str]):
    """Gradio callback：勾選要預覽的檔名 → 顯示合併內容（截斷顯示）。"""
    try:
        if not selected_names:
            return gr.update(value="", visible=False)
        parts = []
        preview_limit = max(2000, MAX_TOKENS_INPUT // 5)
        for name in selected_names:
            txt = cache.get(name, "")
            if len(txt) > preview_limit:
                txt = txt[:preview_limit] + "\n...[內容過長，已截斷顯示]"
            parts.append(f"===== {name} =====\n{txt}")
        return gr.update(value="\n\n".join(parts), visible=True)
    except Exception as e:
        print(f"[SHOW_TOGGLE][ERROR] {e}")
        return gr.update(value=f"[ERROR] {e}", visible=True)

# -----------------------------
# 檢核前驗證 / Prompt 構建
# -----------------------------
def validate_before_run(
    policy_files: List[str],
    self_rules: str,
    self_text: str,
    self_files: List[str],
) -> Tuple[bool, str]:
    """檢核前的前置驗證：內規至少一檔、規則必填、自評文字或附檔至少其一。"""
    try:
        errors = []
        if not (policy_files and len(policy_files) > 0):
            errors.append("請上傳『單位適用內規（可多檔）』至少一個檔案。")
        if not (self_rules and self_rules.strip()):
            errors.append("請輸入『自評檢核規則』。")
        if not (self_text and self_text.strip()) and not (self_files and len(self_files) > 0):
            errors.append("請提供『單位自評』文字，或上傳『單位自評附檔（可多檔）』至少一個。")
        ok = (len(errors) == 0)
        return ok, "\n".join(errors)
    except Exception as e:
        print(f"[VALIDATE][ERROR] {e}")
        return False, f"驗證失敗：{e}"

def build_prompt(rules: str, doc_text: str) -> List[dict]:
    """依規則與文件全文構建 chat messages。"""
    try:
        system_msg = {"role": "system", "content": SYSTEM_MESSAGE}
        user_msg = {
            "role": "user",
            "content": f"{DEMO_PROMPT_INSTRUCTIONS}\n\n[檢核規範]\n{rules}\n\n[文件全文]\n{doc_text}"
        }
        return [system_msg, user_msg]
    except Exception as e:
        print(f"[PROMPT][ERROR] {e}")
        return [{"role": "system", "content": SYSTEM_MESSAGE},
                {"role": "user", "content": "（構建 Prompt 失敗）"}]

# -----------------------------
# 主流程：執行檢核
# -----------------------------
def run_check_with_log(
    policy_files_paths,  # 多檔內規（必填）
    self_rules_text,     # 自評檢核規則（必填；預設載入 DEFAULT_RULES）
    self_text,           # 自評文字（可空）
    self_files_paths     # 多檔自評附檔（可空）
):
    logs: List[str] = []
    try:
        # 驗證
        ok, msg = validate_before_run(policy_files_paths, self_rules_text, self_text, self_files_paths)
        if not ok:
            msg_display = msg.replace("\n", "；")
            logs.append(f"[VALIDATE] {msg_display}")
            return msg, None, None, "\n".join(logs)

        # 紀錄 / 讀取內規（目前僅作為流程留痕）
        for p in (policy_files_paths or []):
            t, info = _read_file_to_text(p)
            logs.append(f"[POLICY] {info}")

        # 取得自評內容（優先文字；否則合併自評附檔）
        doc_text = ""
        if self_text and self_text.strip():
            doc_text = self_text.strip()
            logs.append(f"[SELF] from textbox, len={len(doc_text)}")
        else:
            pieces = []
            for p in (self_files_paths or []):
                t, info = _read_file_to_text(p)
                logs.append(f"[SELF_FILE] {info}")
                if t.strip():
                    pieces.append(t.strip())
            doc_text = "\n\n".join(pieces).strip()
            if not doc_text:
                return "未取得任何自評內容（文字與附檔皆為空）。", None, None, "\n".join(logs)

        # 長度限制（保守）
        if len(doc_text) > MAX_TOKENS_INPUT:
            logs.append(f"[WARN] self text too long ({len(doc_text)}) → truncate to {MAX_TOKENS_INPUT}")
            doc_text = doc_text[:MAX_TOKENS_INPUT]

        # 構建訊息並呼叫本地 LLM
        msgs = build_prompt(self_rules_text.strip(), doc_text)
        logs.append("=== Prompt ===")
        logs.append(f"[system] len={len(msgs[0]['content']) if msgs else 0}")
        logs.append(f"[user] len={len(msgs[1]['content']) if len(msgs)>1 else 0}")

        result_dict = call_llm(msgs, model="local", logs=logs)
        logs.append("[LLM] done.")

        pretty = json.dumps(result_dict, ensure_ascii=False, indent=2)
        table = [
            [i.get("規則編號",""), i.get("規則內容",""), i.get("判斷",""), i.get("理由",""), i.get("建議","")]
            for i in result_dict.get("規則逐點檢核", [])
        ]
        info = f"自評內容長度：{len(doc_text)}；自評檢核規則長度：{len(self_rules_text.strip())}"
        return info, pretty, table, "\n".join(logs)
    except Exception as e:
        logs.append(f"[RUN][ERROR] {e}")
        return f"執行失敗：{e}", None, None, "\n".join(logs)

def toggle_debug(current_visibility: bool):
    """切換 Debug Log 顯示。"""
    try:
        return gr.update(visible=not current_visibility), not current_visibility
    except Exception as e:
        print(f"[DEBUG_TOGGLE][ERROR] {e}")
        return gr.update(), current_visibility

# -----------------------------
# Gradio 介面
# -----------------------------
with gr.Blocks(title="法遵自評覆核（PaddleOCR + LLaMA 本地）") as demo:
    gr.Markdown("# 法遵自評覆核（支援 PDF OCR：繁/簡中文；本地 LLaMA 推論）")
    gr.Markdown("步驟：上傳『單位適用內規（可多檔）』 → 輸入『自評檢核規則』（預設載入 config） → 輸入『單位自評』或上傳『自評附檔（可多檔）』 → 開始檢核。")

    # 內規（多檔）+ 清單 + 個別顯示開關 + 預覽（預設隱藏）
    with gr.Row():
        policy_files_in = gr.Files(
            label="上傳單位適用內規（可多檔，.txt 或 .pdf）",
            file_types=[".txt", ".pdf"],
            type="filepath"
        )
    policy_list_md = gr.Markdown("（尚未上傳任何檔案）")
    policy_show_chk = gr.CheckboxGroup(label="顯示哪些內規內容（個別開關，預設不勾選）", choices=[])
    policy_show_area = gr.Textbox(label="內規檔案內容預覽（僅顯示勾選者，可能截斷）", value="", lines=15, visible=False)
    policy_cache_state = gr.State({})

    # 自評檢核規則（預設由 config 載入，可編輯，視為必填）
    with gr.Row():
        self_rules_in = gr.Textbox(
            label="自評檢核規則（必填；預設載入自 config）",
            value=DEFAULT_RULES,
            lines=8,
            placeholder="請貼上要檢核自評內容的規則；例如：需包含抽檢紀錄、會辦流程、簽核證明等。"
        )

    # 自評文字（可空）
    with gr.Row():
        self_text_in = gr.Textbox(
            label="單位自評（文字輸入；可留白改上傳自評附檔）",
            value="",
            lines=10,
            placeholder="請輸入自評文字；或留白並在下方上傳自評附檔（可多檔）"
        )

    # 自評附檔（多檔）+ 清單 + 個別顯示開關 + 預覽（預設隱藏）
    with gr.Row():
        self_files_in = gr.Files(
            label="上傳單位自評附檔（可多檔，.txt 或 .pdf）",
            file_types=[".txt", ".pdf"],
            type="filepath"
        )
    self_list_md = gr.Markdown("（尚未上傳任何檔案）")
    self_show_chk = gr.CheckboxGroup(label="顯示哪些自評附檔內容（個別開關，預設不勾選）", choices=[])
    self_show_area = gr.Textbox(label="自評附檔內容預覽（僅顯示勾選者，可能截斷）", value="", lines=15, visible=False)
    self_cache_state = gr.State({})

    # 控制列
    with gr.Row():
        run_btn = gr.Button("開始檢核", variant="primary", interactive=False)
        toggle_debug_btn = gr.Button("切換 Debug Log 顯示/隱藏")

    info_out = gr.Markdown(label="讀檔／輸入資訊")
    json_out = gr.Code(label="檢核結果（JSON）", language="json")
    table_out = gr.Dataframe(headers=["規則編號","規則內容","判斷","理由","建議"], wrap=True)
    debug_log = gr.Textbox(label="Debug Log", lines=14, visible=False)
    debug_state = gr.State(False)

    # 內規：清單 + 勾選 + 預覽
    policy_files_in.change(
        fn=on_files_change,
        inputs=[policy_files_in],
        outputs=[policy_list_md, policy_show_chk, policy_show_area, policy_cache_state]
    )
    policy_show_chk.change(
        fn=on_show_toggle,
        inputs=[policy_show_chk, policy_cache_state],
        outputs=[policy_show_area]
    )

    # 自評附檔：清單 + 勾選 + 預覽
    self_files_in.change(
        fn=on_files_change,
        inputs=[self_files_in],
        outputs=[self_list_md, self_show_chk, self_show_area, self_cache_state]
    )
    self_show_chk.change(
        fn=on_show_toggle,
        inputs=[self_show_chk, self_cache_state],
        outputs=[self_show_area]
    )

    # 「開始檢核」互動條件：內規(至少一檔) + 自評規則(必填) + 自評(文字或檔案擇一)
    def _ready(policy_files, self_rules, self_text, self_files):
        try:
            ok, _ = validate_before_run(policy_files, self_rules, self_text, self_files)
            return gr.update(interactive=ok)
        except Exception as e:
            print(f"[READY][ERROR] {e}")
            return gr.update(interactive=False)

    policy_files_in.change(_ready, [policy_files_in, self_rules_in, self_text_in, self_files_in], [run_btn])
    self_rules_in.change(_ready, [policy_files_in, self_rules_in, self_text_in, self_files_in], [run_btn])
    self_text_in.change(_ready, [policy_files_in, self_rules_in, self_text_in, self_files_in], [run_btn])
    self_files_in.change(_ready, [policy_files_in, self_rules_in, self_text_in, self_files_in], [run_btn])

    # Run
    run_btn.click(
        fn=run_check_with_log,
        inputs=[policy_files_in, self_rules_in, self_text_in, self_files_in],
        outputs=[info_out, json_out, table_out, debug_log]
    )
    toggle_debug_btn.click(fn=toggle_debug, inputs=[debug_state], outputs=[debug_log, debug_state])

if __name__ == "__main__":
    # Spaces 需綁 0.0.0.0；本地可保留相同設定
    demo.launch(server_name="0.0.0.0", server_port=int(os.getenv("PORT", "7860")), share=False)